Background:　Magnetic　resonance　imaging　(MRI)　has　played　an　important　role　in　the　rapid　growth　of　medical　imaging　diagnostic　technology,　especially　in　the　diagnosis　and　treatment　of　brain　tumors　owing　to　its　non-invasive　characteristics　and　superior　soft　tissue　contrast.　However,　brain　tumors　are　characterized　by　high　non-uniformity　and　non-obvious　boundaries　in　MRI　images　because　of　their　invasive　and　highly　heterogeneous　nature.　In　addition,　the　labeling　of　tumor　areas　is　time-consuming　and　laborious.　Methods:　To　address　these　issues,　this　study　uses　a　residual　grouped　convolution　module,　convolutional　block　attention　module,　and　bilinear　interpolation　upsampling　method　to　improve　the　classical　segmentation　network　U-net.　The　influence　of　network　normalization,　loss　function,　and　network　depth　on　segmentation　performance　is　further　considered.　Results:　In　the　experiments,　the　Dice　score　of　the　proposed　segmentation　model　reached　97.581%,　which　is　12.438%　higher　than　that　of　traditional　U-net,　demonstrating　the　effective　segmentation　of　MRI　brain　tumor　images.　Conclusions:　In　conclusion,　we　use　the　improved　U-net　network　to　achieve　a　good　segmentation　effect　of　brain　tumor　MRI　images.　©　2023　Beijing　Zhongke　Journal　Publishing　Co.　Ltd